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My research focuses on computing and understanding motion in the world from video. In generic scenes I study optical flow (the 2D image motion) and how it relates to physical properties of the world including 3D shape, material, illumination, and motion. I also develop new methods to capture natural, complex, human and animal motion for applications in computer vision, animation, and neuroscience.

I am interested in computer vision and machine learning with a focus on 3D scene understanding, parsing and reconstruction. During my Ph.D. I have developed probabilistic models for 3D traffic scene understanding from movable platforms.

I am interested in the intersection between computer vision and machine learning with a focus on holistic visual scene understanding. In particular, I am interested in analyzing and modeling people in our complex visual scenes.

My main research interests lie in Bayesian sensor fusion and optimization-based state estimation. The core application focus of my research is vision-based perception in Robotics. I have worked with several ground robots in the recent past. Currently, I am focusing on aerial robots and active perception in multiple aerial vehicle systems.

I work with computer vision researchers to coordinate, schedule and run human subjects trials involving body shape and motion analysis at the Perceiving Systems Department. To collect data we use several computer vision technologies, including our unique 3D and 4D body scanners and our new 4D face scanner.

My research focus and interest is in the area of 3D computer vision and computer graphics. I am especially interested in non-rigid shape analysis, statistical modelling of various kinds of shapes, and the analysis of motion data.

My research focuses on understanding the link between semantics and vision. I believe that our intelligence and ability to perceive our surroundings is strongly influenced by language and meaning. I am also very interested in human emotions, facial expressions, sentiment analysis, multimodal learning, transfer learning and 3D modelling of human bodies and faces; amongst others.

I'm a student assistant supervised by postdoc researcher Timo Bolkart. My work is focusing on gaze behavior extraction and animation. Currently, one of our goals is to integrate human gaze behavior to the face model.

Yinghao Huang is a PhD candidate at Max Planck Institute for Intelligent Systems, supervised by Director Michael J. Black. His research interests fall in the areas of Machine Learning, Computer Vision and Computer Graphics. More specifically, he focuses on the topics of Human Body Modelling, 3D Human Shape and Pose Estimation, and other related things.

Perception is a fundamental part of intelligence since perception is necessary to acquire knowledge and knowledge is necessary to understand perception. Therefore computer vision is one of the most important aspects in the realization of intelligent systems. My interest of research lies in computer vision and the combination with machine learning which, to my mind, will enable the realization of intelligent systems. Currently, I am working on optical flow and how to incorporate high-level information to alleviate this ill-posed problem.

I coordinate our department's research trials. We collect data on human body shape and pose. This includes 3D and 4D body scans, face scans, anthropometric measurements, Motion Capture, and further (experimental) technologies. I recruit participants, schedule, manage appointments, create protocols of poses and movements and gain data according to our scientists' needs. Also, I process the data and take care of data security.
Besides that, I am responsible for organising lab tours for visitors of our department.

My research concerns learning models of perception and production of non-verbal communicative behavior. Such models can be used to create richer human-robot and human-avatar interaction, for medical diagnosis systems, and for contextual synthesis of different kinds of human behaviors, e.g., guiding synthesis of hand motion from body motion.

How can autonomous perception discover high-dimensional patterns in recorded data from our environment? I am approaching this question by working on structured computer vision tasks, such as Human Pose Estimation. I hope that insights from this area will improve our data analysis systems, so that they can assist us in better understanding our environment.

My work focuses on improving human body shape and pose estimation from different sensor based systems. I help develop tools and methods for capturing and processing data from depth/ToF sensors and mocap-like systems. I am also involved in developing tools to implement research projects into existing computer graphics and animation pipelines.

Are there people out there? How do they move? What is their body shape? What are they wearing? For machines to interact with humans and the physical world, we need to train them to answer these questions. My research is focused on combining ideas from computer vision and machine learning to enable machines to perceive humans. During my Ph.D. I worked mostly on geometric modelling and articulated tracking from images.

I work on decomposing photographs into their intrinsic layers of reflectance and shading using deep learning methods for fast inference. In addition I started to work on interactive semantic segmentation using CNNs.

I'm a second year PhD Student at the department of Perceiving Systems.
I'm developing multi-aerial vehicle intelligence for practical research application with prototypes built here at the institute. My current work involves integrating detections from real time deep neural networks into cooperative multi vehicle sensor fusion.

My current research is focused on building probabilistic models on top of deep neural networks for various computer vision tasks. I'm also interested in object detection and recognition, as well as general machine learning algorithms and applications.

My research aims at understanding the world through the capture and analysis of heterogeneous data (MRI, CT, Point clouds, images, ...)
in order to create applied digital instruments, that allow, for example,
to generate novel views from a scene,
to infer the human shape from a clothed scan,
or to predict the amount of adipose tissue of a person from surface observations.
To address this challenge, I adopt the approaches of Computer Vision, Signal Processing,
Computer Graphics and Statistical Models.
My research is often multi-disciplinary, as I need to combine knowledge from these different domains.

I am interested in understanding how we perceive human body shape and pose. Furthermore, I am interested in studying how individual factors (such as culture) affect our perception of our own and other people's bodies.
On the side from research, I enjoy web technologies! I am currently supporting the creation of websites for scientific data acquisition and dissemination related to 3D body shape, as well as web development for scientific experiments and perceptual studies.

One of the requirements for enabling machines to perceive and interact in a human environment is to accurately perceive humans and their activities. My research is related to different aspects of movement perception and modeling. Since completing my PhD I'm focusing in human hand modeling, detection and pose estimation.

My research is based in preclinical imaging at the Werner Siemens Imaging Center, and I am focused on novel molecular imaging techniques. My research involves awake and unrestrained rodents and measurements of a more truthful neurophysiological response (to drugs, stimuli, treatments, etc…). I am interested in building an model for tracking and capturing the most commonly used research rodents in preclinical applications.

My goal is to apply statistical human body models in various research domains such as psychology, cognitive science, and medicine. A primary goal is to make our body software accessible to more people. For this purpose I interact with various research groups who need body data and software for doing experiments. I manage these relationships, and support the transfer of body shapes as needed.

The goal of my research is to understand visual factors that contribute to one’s representation of the physical body. I am using the novel technology of biometric-based avatars provided by the MPI for Intelligent Systems to study mechanisms related to body representations in healthy people and its distortions in eating and weight disordered individuals.

For my PhD I worked with Juergen Gall on Hand-Object Interaction. In particular we focused on capturing the motion of hands interacting with each other and/or with a rigid or an articulated object. We further studied the case of acquiring missing knowledge about the manipulated object, i.e. its shape or its kinematic model.

My research focuses on the computational analysis of video sequences: In what ways can the temporal dimension of videos be used by a computer to better understand the structure of a scene? And what can we learn from dynamic stimuli processing in the human visual system to make our algorithms more robust?

My current research focuses on representing the appearance of people in images and video sequences. I am particularly interested in 2D and 3D models that capture the variability in shape of articulated and deformable objects like the human body.
Previous work focused on color image reproduction, multispectral color imaging, readability of colored text.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems